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Displaying 321 - 325 of 1195Deforestation and fragmentation of natural forests in the upper Changhua watershed, Hainan, China: implications for biodiversity conservation
Hainan, the largest tropical island in China, belongs to the Indo-Burma biodiversity hotspot. The Changhua watershed is a center of endemism for plants and birds and the cradle of Hainan’s main rivers. However, this area has experienced recent and ongoing deforestation and habitat fragmentation. To quantify habitat loss and fragmentation of natural forests, as well as the land-cover changes in the Changhua watershed, we analyzed Landsat images obtained in 1988, 1995, and 2005.
Analysis of Rural Landscape and Land Fragmentation Through GIS in the Gjocaj Commune, Albania
After the downfall of the socialist economic system in 1990, Albania underwent an agriculture reform in 1992. After the reform execution, all agricultural land was redistributed among village residents. The subsequent land segmentation lead to extreme agriculture production implications.Gjocaj Commune is located in the centre of Albania at the Peqin District part of the Elbasan Region, which is a typical agricultural area, very close to Tirana and not far from the Adriatic seashore.
Voluntary Nonmonetary Conservation Approaches on Private Land: A Review of Constraints, Risks, and Benefits for Raptor Nest Protection
Biodiversity conservation on private land of the developed world faces several challenges. The costs of land are often high, and the attitudes of landowners towards conservation are variable. Scientists and practitioners need to scan for and adopt cost-effective solutions that allow for the long-term sustainability of conservation measures on private land. In this study, we focus on one of such possible solutions: Working with landowners to implement voluntary nonmonetary conservation. We restrict our focus to protection of raptor nests, but the ideas can be applied to other taxa as well.
Novel Semi-Supervised Land Cover Classification Technique of Remotely Sensed Images
This research article addresses the problem of land-cover classification from the multi-spectral remotely sensed images using a novel self-training based semi-supervised learning (SSL) technique. The proposed system, instead of using a single classifier, builds an ensemble of classifiers with the hope that the ensemble system will have a lesser generalization error than any of its members.
Prior Knowledge-Based Automatic Object-Oriented Hierarchical Classification for Updating Detailed Land Cover Maps
Automatic information extraction from optical remote sensing images is still a challenge for large-scale remote sensing applications. For instance, artificial sample collection cannot achieve an automatic remote sensing imagery classification. Based on this, this paper resorts to the technologies of change detection and transfer learning, and further proposes a prior knowledge-based automatic hierarchical classification approach for detailed land cover updating. To establish this method, an automatic sample collection scheme for object-oriented classification is presented.